estimate_pars

<code>estimate_pars</code> estimates the distribution's
parameters using their maximum likelihood estimator. This estimate
is conditional on the current xmin value.

An implementation of maximum likelihood estimators
for a variety of heavy tailed distributions, including both the
discrete and continuous power law distributions. Additionally, a
goodness-of-fit based approach is used to estimate the lower cut-off
for the scaling region.

Colin Gillespie

poweRlaw

Analysis of Heavy Tailed Distributions

estimate_pars function

<dl><dt>m</dt>
<dd>A reference class object that contains the data.</dd>
<dt>pars</dt>
<dd>default <code>NULL</code>. A vector or matrix (number of columns equal
to the number of parameters) of parameters used to #' optimise over.
Otherwise, for each value of <code>xmin</code>, the mle will be used, i.e. <code>estimate_pars(m)</code>.
For small samples, the mle may be biased.</dd></dl>

Arguments

Estimates the distributions using mle. — estimate_pars

<dl>

<dt>m</dt>
<dd>A reference class object that contains the data.</dd>


<dt>pars</dt>
<dd>default <code>NULL</code>. A vector or matrix (number of columns equal
to the number of parameters) of parameters used to #' optimise over.
Otherwise, for each value of <code>xmin</code>, the mle will be used, i.e. <code>estimate_pars(m)</code>.
For small samples, the mle may be biased.</dd>

</dl>

estimate_pars: Estimates the distributions using mle.

Description

Usage

Value

Arguments

Examples